We extensively explore GIST by performing multi-class node classification on five publicly available datasets; see Table REF .
We adopt standard training, validation, and testing splits.
In all experiments, we compare the performance of GIST to that of single-GPU baseline models, but comparisons of GIST to other distributed training methodologies (e.g., local SGD [1]} and ensembles) are also provided in the supplementary material.
Cora, Citeseer, and Pubmed are considered “small-scale” datasets and are used to run low-cost ablation experiments; see Section REF .
Reddit and Amazon2M are considered “large-scale” datasets.
F1 score and training time are used to measure the performance of GIST for both of these datasets; see Section REF .
For large-scale datasets, the goal of GIST is to \(i)\)  achieve state-of-the-art performance, \(ii)\)  minimize wall-clock training time, and \(iii)\)  enable experiments with very large GCN models.
